The role of domain knowledge in data mining
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Bugs as deviant behavior: a general approach to inferring errors in systems code
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Bootstrapping an Infrastructure
LISA '98 Proceedings of the 12th USENIX conference on System administration
STRIDER: A Black-box, State-based Approach to Change and Configuration Management and Support
LISA '03 Proceedings of the 17th USENIX conference on System administration
Understanding and dealing with operator mistakes in internet services
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Automatic misconfiguration troubleshooting with peerpressure
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Why do internet services fail, and what can be done about it?
USITS'03 Proceedings of the 4th conference on USENIX Symposium on Internet Technologies and Systems - Volume 4
AutoBash: improving configuration management with operating system causality analysis
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
Using causality to diagnose configuration bugs
ATC'08 USENIX 2008 Annual Technical Conference on Annual Technical Conference
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Detecting large-scale system problems by mining console logs
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
KLEE: unassisted and automatic generation of high-coverage tests for complex systems programs
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Automating configuration troubleshooting with dynamic information flow analysis
OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
Static extraction of program configuration options
Proceedings of the 33rd International Conference on Software Engineering
Context-based online configuration-error detection
USENIXATC'11 Proceedings of the 2011 USENIX conference on USENIX annual technical conference
An empirical study on configuration errors in commercial and open source systems
SOSP '11 Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles
Virtual machine images as structured data: the mirage image library
HotCloud'11 Proceedings of the 3rd USENIX conference on Hot topics in cloud computing
Precomputing possible configuration error diagnoses
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
X-ray: automating root-cause diagnosis of performance anomalies in production software
OSDI'12 Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation
Do not blame users for misconfigurations
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
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As software systems become more complex and configurable, failures due to misconfigurations are becoming a critical problem. Such failures often have serious functionality, security and financial consequences. Further, diagnosis and remediation for such failures require reasoning across the software stack and its operating environment, making it difficult and costly. We present a framework and tool called EnCore to automatically detect software misconfigurations. EnCore takes into account two important factors that are unexploited before: the interaction between the configuration settings and the executing environment, as well as the rich correlations between configuration entries. We embrace the emerging trend of viewing systems as data, and exploit this to extract information about the execution environment in which a configuration setting is used. EnCore learns configuration rules from a given set of sample configurations. With training data enriched with the execution context of configurations, EnCore is able to learn a broad set of configuration anomalies that spans the entire system. EnCore is effective in detecting both injected errors and known real-world problems - it finds 37 new misconfigurations in Amazon EC2 public images and 24 new configuration problems in a commercial private cloud. By systematically exploiting environment information and by learning correlation rules across multiple configuration settings, EnCore detects 1.6x to 3.5x more misconfiguration anomalies than previous approaches.